The obscuring effect of clouds is one of the major factors which restrict the observation capabilities of optical remote sensing satellites. In this paper

aimed at image restoration for optical remote sensing images covered by thin clouds

three contributions have been made. Firstly

with characteristics of clouds

an enhanced linear mixing model has been proposed

in which the influence of clouds on the measured spectra has been presented explicitly. Secondly

two spectral unmixing based image restoration methods have been given

namely the direct elimination method (DEM) and the abundance adjusting method (AAM). At last

with different combinations of the two spectral unmixing algorisms VCA/MDC-NMF and two image restoration methods DEM/AAM

the capabilities and results of relevant methods are analyzed qualitatively and quantitatively

using both simulated and real datasets. Experimental results show that

the combination of MDC-NMF and AAM can achieve the best image restoration result.